Learning Cause Identifiers from Annotator Rationales

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
Learning Cause Identifiers from Annotator Rationales
Muhammad Arshad Ul Abedin and Vincent Ng and Latifur Rahman Khan
Department of Computer Science
University of Texas at Dallas
Richardson, TX 75080-3021
in the text. One way to solve the data paucity problem is, of
course, to annotate more data, but obtaining human-annotated
data could be expensive. Interestingly, using the annotator
rationales may provide an answer to these two questions.
An annotator rationale, as used by Zaidan et al. [2007], is
the fragment of text that motivated an annotator to assign a
particular label to a document. In their work on classifying
the sentiment expressed in movie reviews as positive or negative, they generate additional training instances by removing
rationales from documents. Since these pseudo-examples,
known as contrast examples, lack information that the annotators thought as important, the learning algorithm should
be less confident about the label of these weaker instances.
A learner that successfully learns this difference in confidence assigns a higher importance to the pieces of text that
are present only in the original instances. Thus the contrast
examples help the learning algorithm both by providing indication to which parts of the documents are important and by
increasing the number of available training instances. However, their approach explores only one way such rationales
can be utilized.
We propose two ways to utilize the rationales for cause
identification in a supervised learning framework. Specifically, the rationales are used to generate (1) new features and
(2) a new type of pseudo-examples for the learner. This new
type of pseudo-examples, called the residue examples, complements Zaidan et al.’s (2007) contrast examples in that they
contain only the rationales present in a document. We motivate the use of residue examples in Section 4.
Experimental results on a set of manually annotated ASRS
reports suggest that rationales are indeed helpful for cause
identification. Using rationales as features in addition to generating both types of pseudo-examples, we find an improvement of over 7% in absolute F-score compared to a unigram
SVM baseline that does not employ rationales.
The rest of the paper is organized as follows. In Section 2
we discuss the ASRS dataset. Section 3 describes Zaidan
et al.’s [2007] framework for exploiting annotator rationales.
We discuss and evaluate our own approach to utilizing the rationales in the cause identification problem in Sections 4 and
5, respectively. Finally, we perform a detailed error analysis
in Section 6 and conclude in Section 7.
In the aviation safety research domain, cause identification refers to the task of identifying the possible causes responsible for the incident described
in an aviation safety incident report. This task
presents a number of challenges, including the
scarcity of labeled data and the difficulties in finding the relevant portions of the text. We investigate
the use of annotator rationales to overcome these
challenges, proposing several new ways of utilizing rationales and showing that through judicious
use of the rationales, it is possible to achieve significant improvement over a unigram SVM baseline.
The Aviation Safety Reporting System (ASRS) was established by NASA in 1976 to collect voluntarily submitted reports about aviation safety incidents written by flight crews,
attendants, controllers and other related parties. Each report contains a free text narrative that describes, among other
things, the cause of the incident. Knowing the causes of the
incidents can play a significant role in developing aviation
safety measures. As a result, we seek to automatically identify the causes of the incident described in a report narrative
from a set of 14 possible causes, or shaping factors, identified
by the NASA researchers [Posse et al., 2005]. Since several
factors may have caused an incident, cause identification is a
multi-class, multi-labeled text classification problem.
This under-studied problem presents two major challenges
to NLP researchers. One is the scarcity of labeled data: The
original ASRS reports were not labeled with the shaping factors, and only a small part of it has so far been manually annotated, which makes it difficult for a supervised learning algorithm to generate an accurate model for all the shaping factors. Another is that the narratives chiefly describe the incidents that are occurring, while parts of the reports discussing
the actual causes may be very small, which means the learner
has to be careful as to which features it chooses as useful.
This gives rise to two challenging questions: (1) how to solve
the paucity of labeled data problem, and (2) how to enable the
learner to make better use of the small pieces of clues lying
Annotator A1
Annotator A2
Class Rationales Overlap Rationales Overlap
with A2
with A1
12 75.0%
9 100.0%
22 77.3%
17 100.0%
6 50.0%
3 100.0%
8 87.5%
9 77.8%
26 42.3%
15 73.3%
42 50.0%
34 61.8%
16 75.0%
12 100.0%
9 44.4%
9 44.4%
10 100.0%
11 90.9%
25 32.0%
12 66.7%
64 37.5%
28 85.7%
11 72.7%
10 80.0%
6 100.0%
13 46.2%
257 54.5%
182 76.9%
The data set used here is based on the ASRS data set previously created by us [Abedin et al., 2010]. It contains 1333
reports labeled with shaping factors1 (see the column “Shaping Factor” in Table 1 for the list of the 14 shapers2 ). These
reports are divided into three subsets: a training set containing 233 reports, a development set containing 100 reports and
a test set containing 1000 reports. Since we need reports
annotated with rationales, we decided to generate some additional training data, choosing 1000 unlabeled reports randomly from those we provided in Abedin et al. [2010] and
adding them to the training set. Then we annotated the newlyadded reports with shaping factors, and identified annotation
rationales for all the reports in the training set. Below, we
discuss the procedures in annotating a report with shaping
factors (Section 2.1) and rationales (Section 2.2).
Annotation With Shaping Factors
Table 2: Inter-annotator agreements for rationales.
We followed the same annotation procedure described in
Abedin et al. [2010] to assign shaping factor labels to the
1000 unlabeled reports that we added to the training set. One
author (A1) and one student worker (A2) independently annotated 100 randomly chosen unlabeled reports, using the descriptions of the shaping factors in Posse et al. [2005] and
Ferryman et al. [2006]. The inter-annotator agreement, calculated using the Krippendorff’s (2004) α statistics as described
by Artstein and Poesio [2008] with the MASI scoring metric
[Passonneau, 2004], was found to be 0.82. The annotators
then resolved each disagreement through discussion. Given
the high agreement rate, we had A1 annotate the remaining
reports. Thus, at the end of the annotation process, we had a
training set with 1233 reports labeled with shaping factors.
1233 training reports, for an average of 4.4 rationales per
document. To get a better sense of these rationales, we list
the five most frequently-occurring rationales annotated for
each shaping factor in Table 1. Since rationales are meant to
improve classifier acquisition, the test reports do not require
(and therefore do not contain) any rationale annotations.
Rationales in Sentiment Classification
In this section, we describe Zaidan et al.’s [2007] approach to
training an SVM with annotator rationales for classifying the
sentiment of a movie review as positive or negative.
Let xi be the vector representation of document Ri . Given
the rationale annotations on a positive example xi for the
SVM learner, Zaidan et al. construct one or more not-sopositive contrast examples vij . Specifically, they create vij
by removing rationale rij from Ri . Since vij lacks evidence
that an annotator found relevant for the classification task,
the correct SVM model should be less confident of a positive
classification on vij .
This idea can be implemented by imposing additional constraints on the correct SVM model, which can be defined
in terms of a weight vector w. Recall that the usual SVM
constraint on positive example xi is w · xi ≥ 1, which ensures that xi is on the positive side of the hyperplane. In
addition to these usual constraints, we desire that for each j,
w · xi − w · vij ≥ μ, where μ ≥ 0. Intuitively, this constraint
specifies that xi should be classified as more positive than vij
by a margin of at least μ.
Let us define the annotator rationale framework more formally. Recall that in a standard soft-margin SVM, the goal is
to find w and ξ to minimize
|w|2 + C
Annotation With Rationales
The same two annotators, A1 and A2, went through the same
100 reports and answered the question below for each report:
For each shaping factor identified for the incident
described in the report, is there a fragment of text
that is indicative of that shaping factor?
If so, we provide A1 and A2 with the same instructions that
Zaidan et al. had for their annotators for marking up the rationales (see Section 4.1 of their paper). Briefly, the annotators
are asked to “do their best to mark enough rationales to provide convincing support for the class of interest”, but are not
expected to “go out of their way to mark everything”.
The inter-annotator agreement for the rationales were calculated in the same manner as Zaidan et al. [2007], where
two rationales are considered overlapping if they have at least
one word in common. In the 100 reports, A1 annotated 257
rationales, 54.5% of which overlap with A2’s rationales, and
A2 identified 182 rationales, with a 76.9% overlap. Statistics
for all the classes are shown in Table 2. A1 then proceeded
to annotate the rationales for the remaining 1133 reports in
training set, resulting in a total of 5482 rationales over the
subject to
See http://www.utdallas.edu/∼maa056000/asrs.html
Space limitations preclude the inclusion of the definitions of
these shaping factors. See Abedin et al. [2010] for details.
∀i : ci w · xi ≥ 1 − ξi ,
ξi > 0,
Shaping Factor
Communication Environment
Duty Cycle
Physical Environment
Physical Factors
Resource Deficiency
attitude (24), complacency (13), complacent (6), playing a game (2), overconfident (2)
noise (28), no response (18), did not hear (14), static (12), congestion (12)
last leg (7), last night (4), reduced rest (3), longitude duty days (2), longitude duty day (2)
new (123), unfamiliar (16), aligned (9), not familiar (5), very familiar (4)
bright lights (2), wall of white (1), black hole (1)
weather (144), visibility (77), turbulence (51), clouds (43), winds (38)
fatigue (25), tired (14), sick (9), fatigued (7), very tired (5)
busy (78), attention (76), distraction (30), distracted (17), DISTRS (9)
late (56), pressure (45), expedite (14), short time (12), rushed (12)
training (57), mistake (42), inadvertently (27), mistakes (18), forgotten (11)
off (410), more (194), further (133), damage (85), warning (84)
very busy (13), solo (13), extremely busy (9), many aircraft (8), single pilot (7)
surprised (14), suddenly (13), unexpected (7), unusual event (2), unknown to me (2)
Resolution Advisory (59), confusion (58), confused (21), confusing (13), UNCLR (9)
Table 1: Five most frequently-occurring rationales associated with each shaping factor and their frequencies of occurrences.
where xi is a training example; ci ∈ {−1, 1} is the class label
of xi ; ξi is a slack variable that allows xi to be misclassified
if necessary; and C > 0 is the misclassification penalty. To
enable this standard soft-margin SVM to also learn from contrast examples, Zaidan et al. add contrast constraints:
is 1 if the string representing the rationale appears in the given
report and 0 otherwise.
We use rationales to provide two types of additional training examples: contrast examples and residue examples.
To use rationales to generate contrast examples, we follow
Zaidan et al.’s approach, as described in Section 3. However,
there is a caveat. In Zaidan et al.’s framework, rationales are
used to generate both positive and negative examples, since
rationales were collected from both positive and negative reviews. On the other hand, in cause identification, since each
report can be labeled with more than one shaping factor, the
presence of a rationale for shaper sj in a report does not
provide evidence that the report should not be labeled with
shaper si , where i = j. Rather, it is the absence of rationales
for si rather than the presence of rationales for sj that indicates that the report should not be labeled with si . Hence,
when recasting cause identification as binary classification
tasks, the absence of rationales for the negative examples implies that we will only generate positively-labeled contrast
examples from the rationales. In particular, these positivelylabeled contrast examples are generated to train each binary
classifier in the same way as in Zaidan et al.’s framework.
Now, recall that we propose to a new type of training examples from the rationales: residue examples. Hence, Zaidan et
al.’s framework needs to be modified so that the SVM learner
can take into account the residue examples. Before describing
the modifications, let us motivate the use of residue examples.
Recall from Section 2.2 that the rationale annotators were not
expected to “go out of their way” to identify all the rationales
in a document. Hence, any amount of rationale annotation
is unlikely to mark all the relevant portions of the text, and
thus, the text outside of the rationales may also contain some
relevant portions. Thus, we propose residue examples, which
are examples that contain only the rationales. Specifically,
for each rationale rij appearing in training example xi , we
generate one (positively-labeled) residue example, rij , which
contains only the features extracted from rij . In a sense, the
contrast examples help the learner understand the importance
of the rationale, whereas the residue examples aid in learning
the importance of the rest of the text.
As with contrast examples, residue examples intuitively
∀i, j : w · xi − w · vij ≥ μ(1 − ξij ),
where vij is the contrast example created from Ri by remov
ing rationale rij , and ξij ≥ 0 is the slack variable associated
with vij . These new slack variables should have their own
misclassification cost, so a new term is added to the SVM
objective function, which becomes:
|w|2 + C
ξi + C
Here, C is the misclassification
cost associated with the new
slack variables ξij .
Rationales in Cause Identification
Before describing how we employ rationales for cause identification, recall that cause identification is a 14-class classification task. To employ Zaidan et al.’s annotator rationale
framework, which relies on SVM training and hence assumes
only positive and negative examples, we recast cause identification as a set of 14 binary classification problems, one
for predicting each shaper. More specifically, in the binary
classification problem for predicting shaper si , we create one
training example xi from each document in the training set,
labeling it as positive if the document has si as one of its
labels, and negative otherwise, In essence, we are adopting a
one-versus-all scheme for creating original training instances.
Next, we describe how we can employ rationales for cause
identification. We use rationales to provide additional features and additional training examples. Using rationales to
create additional features is fairly straightforward. It involves
(1) constructing a special lexicon that contains all the rationales in the training data, (2) creating one feature for each
rationale in the lexicon, and (3) augmenting the feature set
with these rationale features. The value of a rationale feature
Expt Feature Set
SVM Baseline
50.8 36.2 42.2
SVM with Rationales as Features
54.8 34.8 42.5
Unigrams, Rationales 56.0 38.8 45.8
Staelin [2002] to find the parameter combination that gives
the best F-score on the development set. Specifically, we
specify the search space of each parameter, and the algorithm
specifies several points of evaluation. At each point, the SVM
models are learned from the training data using the parameter
values at that point, and the learned models are used to classify the development test set instances. The point at which the
best performance (F-score) is observed is then selected as the
new center of search and the search space ranges are halved.
This search process is repeated 5 times. The models built using the best parameter combination after 5 iterations are then
applied to the test set.
Table 3: Results of the baseline and the SVM approach with
rationales as features.
contain less relevant information for classification than the
original documents, and hence the SVM model should also
be less sure of their class values than the original documents.
To capture this intuition in Zaidan et al.’s framework, we add
residue constraints:
Using rationales as features. Results of using the rationales as features are shown in rows 2 and 3 of Table 3. Note
that the frequency threshold of 3 mentioned earlier was employed to filter the unigram features but not the rationale features. When only the rationales are present in the feature set,
the SVM model performs marginally better than the baseline, achieving an F-score of 42.5. However, when rationales
are used as additional features to augment the unigram-based
feature set, the F-score increases considerably to 45.8. This
shows that the rationales are useful when used as features.
∀i, j : w · xi − w · rij ≥ μ(1 − ξij ),
where rij is the residue example created from xij by retaining
only rationale rij , μ ≥ 0 is the size of the minimum separa
tion between original and residue examples, and ξij ≥ 0 is
the slack variable associated with rij . These slack variables
should have their own misclassification cost, so a new term is
added to Zaidan et al.’s objective function, which becomes:
|w|2 + C
ξi + C
ξij + C
Using rationales as pseudo-examples. Results of using the
rationales to generate pseudo-examples are shown in Table 4.
As we can see, using only contrast examples (row 4) does not
improve much upon the SVM baseline. Interestingly, using
our proposed residue examples (row 5) improves the SVM
baseline considerably to F=49.0. When both types of pseudoexamples are applied (row 6), F-score drops to 47.5, which
still performs well above the baseline, however.
Next, we repeat the above experiments, but augment the
feature set with rationales. Comparing the three pairs of experiments (rows 4 and 7; rows 5 and 8; rows 6 and 9), we can
see that using rationales both as features and as contrast examples consistently yields better performance than using rationales only as contrast examples. Hence, rationale features
are useful for cause identification in the presence of contrast
Finally, when rationales are used as features, neither
residue examples nor contrast examples are more effective
than the other in improving the SVM model (rows 7 and 8),
but employing both types of pseudo-examples enables the
model to achieve substantially better performance (row 9).
Here, C is the misclassification cost associated with ξij .
In this section, we evaluate the usefulness of rationales for
cause identification in a supervised learning framework.
Baseline Results
Following Zaidan et al. [2007], our baseline uses SVMlight
[Joachims, 1999] with a linear kernel to train one-versusall classifiers in conjunction with a feature set comprising
only unigrams on the 1233 training reports without pseudoexamples. Numbers, punctuations, stopwords, and tokens appearing less than 3 times in the training set are excluded from
the feature set. The feature values are all boolean: if the token appears in the report, its value is 1 and 0 otherwise. The
only SVM learning parameter in this case is C, the misclassification penalty, which was selected as the one that achieved
the best performance on the 100-report development set (see
Section 2). Results are expressed in terms of precision (P)
and recall (R), both of which are micro-averaged over those
of the 14 binary classifiers, as well as F-score (F). As we can
see from row 1 of Table 3, the baseline achieves an F-score of
42.2 on the 1000-document test set.
Using rationales as pseudo-negatives. So far we have only
used rationales to provide pseudo positive examples. A natural question is: while we do not have rationales that explain why a document should not be labeled with a particular shaper, is it still possible to generate pseudo negative examples? We experiment with a simple idea for generating
pseudo-negatives: to train the binary classifier for predicting shaper si , we create negatively-labeled contrast examples
from the rationale annotations in the negative examples in the
same way as we create positively-labeled examples from the
rationales in the positive examples. The next question, of
course, is: since the rationales in the negative examples are
not necessarily indicators that a document should not be labeled as si , will these negative contrast examples be useful?
To answer this question, we repeat the experiments in Table 4,
Results Using Rationales
Parameter tuning. We tune the cost parameters C, C and
C , and the pseudo-example parameter μ to maximize the
F-score of the SVM model on the development set. Due to
the large number of parameters, we use the design of experiment-based search method for parameter tuning proposed by
Expt Feature
49.9 38.1 43.2
36.9 72.8 49.0
36.4 68.5 47.5
52.2 40.7 45.7
Unigrams, Contrast
Unigrams, Residue
Unigrams, Contrast,
rationales Residue
False Positives
Concept present but not contributing to incident
Wrong context
Bad feature
Too general feature
Wrong word sense
Hypothetical context
Ambiguous feature
55.9 38.8 45.8
Table 5: Error analysis findings for false positives.
39.7 66.0 49.5
zero then it is classified as positive, otherwise it is labeled
negative. Hence, to analyze the causes of the false positives
and the false negatives, we need to look at the actual features
values present in the document vector and their weights in the
hyperplane vector. However, even with a subset of 100 reports, this means looking at more than 60,000 feature values,
and thus we confine ourselves to looking at only the highest
contributing features. In other words, for the false positives,
we look at the feature with the highest positive contribution
to the distance, and for the false negatives, we look at the one
making the biggest negative contribution.
Table 4: Results of approaches using pseudo-examples.
but augment the training set in each experiment with negative
contrast examples. Our preliminary results indicate that additional employing pseudo-negatives yields an improvement
of 1.0–2.6 in F-score, but further experiments are needed to
precisely determine their usefulness.
Generating pseudo-examples from multiple rationales.
So far each pseudo-example is generated from exactly one
rationale. It is conceivable that a contrast example can be generated by removing multiple rationales and a residue example
can be generated by retaining multiple rationales. Adding this
flexibility to the generation of pseudo-examples may result in
an explosion in the number of pseudo-examples, however. As
a result, we limit the number of pseudo-examples that can be
generated for each training report. Specifically, to generate
for a report with r rationales, we (1) choose
l such that l rl ≤ 50, and (2) remove/retain rationale combinations of sizes 1, 2, . . ., l from the report when generating
contrast/residue examples. Adding this flexibility to the generation of positive and negative contrast and residue examples, we achieve an F-score of 52.9 in an experiment where
both unigrams and rationales are employed as features. This
preliminary result indicates that it may be beneficial to generate pseudo-examples from multiple rationales.
False Positives
We analyzed the 62 false positive errors made by the system
and discovered several different reasons why the top positive
contributing features actually misled the learner. These reasons are summarized in Table 5 and discussed in detail below.
Concept present but not contributing to incident. In several cases, the top positive contributing feature is in fact relevant to the shaping factor, but in the specific report, it is not
contributing to the incident in the opinion of the annotator.
For example, the term “busy” is a feature that is relevant to the
shaping factor Preoccupation, but not every report in which
this term appears has Preoccupation as a shaper. For example, Report#230334 contains the sentence “CLBING to cruise
altitude we were cleared to FL230 by a busy controller.” Even
though the controller was evidently busy, that did not contribute to the occurrence of the incident.
Error Analysis
To better understand why the systems fail to correctly classify a number of test instances, we analyze the errors made
by the best-performing system (i.e., the system that yielded
an F-score of 52.9) on a set of 100 reports chosen randomly
from the test set. We investigate two types of errors, namely
false positives, in which the system labels a report as positive
but the annotator does not, and false negatives, in which the
annotator labels a report as positive but the system does not.
62 Percentage
Wrong context. In this category of errors, the feature is in
fact relevant to the shaping factor, but it appears in such a
context that does not imply the shaping factor. For example,
the feature “damage” related to the shaping factor Resource
Deficiency appears in Report#389873 in the following context: “minimal damage.” In this context, the word appears as
an effect of what happened, not as a cause. The SVM learner
is unable to make this distinction, and the feature ends up
contributing heavily to the positiveness of the document.
Analysis Method
Bad feature. There are several cases in which the feature
is not intuitively relevant to the shaping factor, but were assigned high weights by the learner merely because they happened to occur frequently enough in the positive examples
in the training set. For example, the feature “ability” is the
top positive contributor for the shaping factor Attitude in Report#101846, as in the sentence “aircraft performance charts
and PDCS affirmed ability to do so”, but it is clear that the
word “ability” has nothing to do with an attitude problem.
Analyzing the errors made by an SVM learner is a rather
daunting task since the actual reason of why a particular instance is classified as positive or negative is buried under the
vectors representing the separating hyperplane and the document being classified. The SVM learner takes the dot product
of these two vectors (and adds the bias term if biased hyperplanes are being used) to find the distance of the instance from
the separating hyperplane, and if the distance is greater than
Too general feature. There are some words that are related
to the shaping factor in a very general manner, but because
of this generality, may appear in any report without actually
indicating the shaping factor. Similar to the bad features described above, the learner assigns them a high weight because
of their frequent occurrence in the positive reports. For example, the feature “weather” is related to the shaping factor
Physical Environment in a general sense since this shaping
factor is chiefly related to the weather elements hampering a
pilot, but in the context of Report#325010, where it appears
in the sentence “the weather was clear with good visibility
for the los angeles area”, it is merely appearing to describe
the weather conditions.
problem by assigning high negative weights to features important to other classes, while in other cases it simply focuses
on random words that appear in the negative examples more
than the positive ones. The first approach creates false negatives when that other shaping factor is also present in the test
document (as we discuss in the next paragraph), while the
second approach mostly selects bad features.
Feature associated with other labels of the document We
take each feature and find the shaping factor for which the
feature has the highest positive weight in the vector representation of the hyperplane. Then, when we look at the false
positive example, we find the highest negative contributing
feature, and look up its most relevant shaping factor. Then
we see if this shaping factor is also present in the set of labels assigned to the report. In 36 of the 87 false positives we
analyzed, we found that the feature is affiliated with one of
the other shaping factors assigned to the report. Thus, since
the features relevant to the other shaping factors have been
assigned negative weights, their presence in the report makes
them contribute negatively even though they are present because of the presence of other shaping factors in the report.
Wrong word sense. In this case the one sense of the feature is relevant to the shaping factor, but it appears in a different sense in the falsely positive report. For example, Report#642907 is labeled as a positive example for class Attitude, and the feature with the highest positive contribution is
the word “attitude”. This word is apparently a good indicator
for this class as this has also been identified by the annotators
as a rationale for this class. However, this is true only as long
as it means the mental state of someone. In this particular
report, this word appears in the following context: “I became
slightly disoriented and got into a dangerous attitude.” In this
context the word “attitude” is actually used to mean the orientation of the aircraft and not the mental state of the pilot.
The SVM learner understandably cannot make the difference
between these two meanings and this feature makes a high
contribution to the false positive label of the document.
Others. In one case, the word “tired”, a good feature for
the shaper Physical Factors, appears in Report#534432 in a
hypothetical context: “I feel this could happen to a pilot especially if he was single pilot; behind on the approach; a little
rusty; tired; etc. ...”. In another case, the top positive contributor is the feature “fatigue”, which is relevant to two shapers,
Duty Cycle and Physical Factors. Thus the report gets labeled
positive for Duty Cycle, resulting in a false positive.
We have proposed to use annotator rationales for cause identification, suggesting two novel ways to use rationales — as
features and for generating a new type of pseudo-examples,
namely residue examples. Experimental results on a subset of
the ASRS reports demonstrated the usefulness of rationales
for cause identification. Overall, an SVM learner that exploits rationales substantially improves one that does not by
7.3% in F-score. Moreover, we believe that our detailed analysis of the errors made by our system can provide insights
into the problem as well as directions for future research.
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False Negatives
In the analysis of the false negatives, we look at the highest
negative contributing feature for the reports falsely labeled
as negatives. However, in this case it is harder to identify
whether the top contributing feature is a good negative feature
or not. For example, it is easier to judge whether a given
feature is relevant to a given shaping factor, but how do we
judge whether a feature would be a good indicator for the
shaping factor not being present?
This exposes one interesting property of our problem of
cause identification. When a shaping factor is present, it reflects in the report as such, and by utilizing the clues in the
document, it is possible to identify its presence to a reasonable degree. However, when the shaping factor is not present,
these is rarely any clue. In other words, the person writing the
report usually does not discuss the shaping factors that were
not present. When we look at the top negative contributing
features for the false negatives, we find that the SVM learner
facing this issue. In some of the cases, it tries to solve the